Efficient constructive techniques for training Switching Neural Networks

A general constructive approach for training neural networks in classification
problems is presented. This approach is used to construct a particular connectionist
model, named Switching Neural Network (SNN), based on the conversion
of the original problem in a Boolean lattice domain. The training of an SNN can
be performed through a constructive algorithm, called Switch Programming (SP),
based on the solution of a proper linear programming problem. Since the execution
of SP may require an excessive computational time, an approximate version of it,
named Approximate Switch Programming (ASP) has been developed. Simulation results
obtained on the StatLog benchmark show the good quality of the SNNs trained
with SP and ASP.